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The Ten Most Significant Science Stories of 2022

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Wednesday, December 28, 2022

From Omicron’s spread to a revelation made using ancient DNA, these were the biggest moments of the past year

From Omicron’s spread to a revelation made using ancient DNA, these were the biggest moments of the past year

From Omicron’s spread to a revelation made using ancient DNA, these were the biggest moments of the past year
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SpaceX Dragon Spacecraft Docks to Space Station With New Science and Supplies

While the International Space Station (ISS) was traveling more than 262 miles over the South Atlantic Ocean, a SpaceX Dragon cargo spacecraft autonomously docked to station’s...

The SpaceX Dragon cargo spacecraft docks to the International Space Station’s Harmony module at 7:19 a.m. EDT on Saturday, March 23. Credit: NASA TVWhile the International Space Station (ISS) was traveling more than 262 miles over the South Atlantic Ocean, a SpaceX Dragon cargo spacecraft autonomously docked to station’s Harmony module at 7:19 a.m. EDT on March 23, with NASA astronauts Loral O’Hara and Michael Barratt monitoring operations from the station.The Dragon launched on SpaceX’s 30th contracted commercial resupply mission for NASA at 4:55 p.m. EDT, March 21, from Space Launch Complex 40 at Cape Canaveral Space Force Station in Florida. After Dragon spends about one month attached to the space station, the spacecraft will return to Earth with cargo and research.Among the science experiments Dragon is delivering to the space station are: Fully assembled Nanoracks-Killick-1 CubeSat with its Global Navigation Satellite System Reflectometry (GNSS-R) antenna deployed. Nanoracks-Killick-1 measures sea ice using GNSS-R. Potential applications of GNSS-R include providing data for weather and climate models and improving understanding of ocean phenomena such as surface winds and storm surge. Credit: C-CORE and Memorial University.Monitoring Sea Ice Thickness and Wave Height(Nanoracks-Killick-1) is a CubeSat that measures sea ice parameters using Global Navigation Satellite System (GNSS) reflectometry or reflected signals. This monitoring system could contribute to a better understanding of important ocean phenomena and improved weather and climate models.CSIRO Project Lead Marc Elmouttie with the MRS hardware and Astrobee robot ready for final pre-flight testing. Credit: NASANew Sensors for ASTROBEEThe Multi-resolution Scanner (MRS) Payload for the Astrobee (Multi-Resolution Scanning) tests a new set of sensors to support automated 3D sensing, mapping, and situational awareness functions. These systems could support future Gateway and Lunar surface missions by providing automated defect detection, automated and remote maintenance, and autonomous vehicle operations.A capstone student assembles the microscope and fluid breadboard for the Nano Particle Haloing Suspension payload. This payload tests the controlled assembly of nanoparticles in a solution of zirconia and titanium-dioxide-coated silica. Effective demonstration could lead to applications in an enhanced solar cell generation technology known as quantum-dot solar synthesis. Credit: University of LouisvilleImproving Efficiency of Quantum-Dot Solar CellsThe Nano Particle Haloing Suspension payload tests the controlled assembly of nanoparticles in a liquid solution. A process called nanoparticle haloing uses charged nanoparticles to enable precise particle arrangements that improve the efficiency of quantum-dot synthesized solar cells. Conducting these processes in microgravity provides insight into the relationship between shape, charge, concentration, and interaction of particles.Brachypodium and Setaria were grown in the Plant Growth Systems (PGS) and tested under International Space Station environmental conditions using the Veggie units at NASA’s Kennedy Space Center during the APEX-09 Experiment Verification Test. Credit: Pubudu HandakumburaObserving Photosynthesis in SpaceAdvanced Plant Experiment-09 (APEX-09), also known as C4 Photosynthesis in Space, observes carbon dioxide capture and mechanisms in two types of grasses. Researchers hope to learn more about photosynthesis and plant metabolism changes overall in space. Knowledge gained could support development of bioregenerative life support systems on future missions.These are just a few of the hundreds of investigations currently being conducted aboard the orbiting laboratory in the areas of biology and biotechnology, physical sciences, and Earth and space science. Advances in these areas will help keep astronauts healthy during long-duration space travel and demonstrate technologies for future human and robotic exploration beyond low-Earth orbit to the Moon through NASA’s Artemis missions and eventually Mars.

Science Simplified: What Is Artificial Intelligence?

What Is Artificial Intelligence? Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain’s...

Artificial intelligence, particularly through machine learning, is revolutionizing how we solve complex problems in various fields including science, medicine, and technology. Facilities like Argonne National Laboratory are leading these advancements, using AI to predict complex system behaviors, improve material selection, and assist in global challenges like disease and climate change.What Is Artificial Intelligence?Artificial intelligence (AI) is the collective term for computer technologies and techniques that help solve complex problems by imitating the brain’s ability to learn.AI helps computers recognize patterns hidden within a lot of information, solve problems, and adjust to changes in processes as they happen, much faster than humans can.VIDEOIn this Science 101: What is Artificial Intelligence video, Argonne National Laboratory scientists Taylor Childers and Bethany Lusch discuss AI — the computer technologies and techniques that help solve complex problems by imitating the brain’s ability to learn. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine, and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams. Researchers use AI to be better and faster at tackling the most difficult problems in science, medicine, and technology, and help drive discovery in those areas. This could range from helping us understand how COVID-19 attacks the human body to finding ways to manage traffic jams.Many Department of Energy (DOE) facilities, like Argonne National Laboratory, assist in developing some the most advanced AI technologies available. Today, they are used in areas of study ranging from chemistry to environmental and manufacturing sciences to medicine and the universe.AI is used to help make models of complex systems, like engines or weather, and predict what might happen if certain parts of those systems changed — for example, if a different fuel was used or temperatures increased steadily.But there are many more uses for AI.A key tool in Argonne’s AI toolbox is a type of technique called machine learning that gets smarter or more accurate as it gets more data to learn from. Machine learning is really helpful in identifying specific objects hidden within a bigger, more crowded picture.In a popular example, a machine learning model was trained to recognize the main features of cats and dogs by showing it many images. Later, the model was able to identify cats and dogs from pictures of mixed animals.Similar machine learning models can help scientists identify, for example, one type of galaxy from another when they receive object-packed images from space telescopes.Machine learning is just one of many AI techniques that help us learn more quickly and accurately. They can help choose the right molecule or chemical for a new material and may one day guide new experiments on their own.Argonne has worked with many organizations around the world to become a leader in artificial intelligence use and development, this includes applying AI to:Improve battery life for cars and energy.Build better climate models that can predict wildfires, hurricanes, and other disasters, and help our communities and power companies protect against them.Find those parts of viruses that attack our cells and develop drugs to fight them.Credit: Argonne National LaboratoryWhat is Artificial Intelligence?Analyzing large complex data to perform human tasks at computer speeds.Artificial intelligence (AI) is now a part of our daily lives, helping to simplify basic tasks, such as voice recognition, content recommendations or photo searches based on people or objects they contain. Scientists are using AI in similar ways to advance our understanding of the world around us. It can help them analyze mountains of data faster, and has provided better solutions. Different AI techniques are used in many research areas, from materials science and medicine to climate change and the cosmos.For example, we can train AI to recognize complex patterns by viewing many different examples. Researchers can use this capability to find new and improved materials for things like solar cells or medicine by training AI on all the known materials for that application. Then AI can help researchers zero in on other promising materials that can be fabricated and tested in a laboratory.

When Algorithms Deliver: The AI Revolution in Logistics

A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation. While Santa Claus may...

A new technique combining machine learning with traditional optimization has been shown to accelerate the solution-finding process of mixed-integer linear programming solvers by up to 70%, enhancing efficiency in logistics and other sectors. Credit: SciTechDaily.comA new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.While Santa Claus may have a magical sleigh and nine plucky reindeer to help him deliver presents, for companies like FedEx, the optimization problem of efficiently routing holiday packages is so complicated that they often employ specialized software to find a solution.This software, called a mixed-integer linear programming (MILP) solver, splits a massive optimization problem into smaller pieces and uses generic algorithms to try and find the best solution. However, the solver could take hours — or even days — to arrive at a solution. The process is so onerous that a company often must stop the software partway through, accepting a solution that is not ideal but the best that could be generated in a set amount of time.Accelerating Solutions With Machine LearningResearchers from MIT and ETH Zurich used machine learning to speed things up.They identified a key intermediate step in MILP solvers that has so many potential solutions it takes an enormous amount of time to unravel, which slows the entire process. The researchers employed a filtering technique to simplify this step, and then used machine learning to find the optimal solution for a specific type of problem.Their data-driven approach enables a company to use its own data to tailor a general-purpose MILP solver to the problem at hand.This new technique sped up MILP solvers between 30 and 70 percent, without any drop in accuracy. One could use this method to obtain an optimal solution more quickly or, for especially complex problems, a better solution in a tractable amount of time.This approach could be used wherever MILP solvers are employed, such as by ride-hailing services, electric grid operators, vaccination distributors, or any entity faced with a thorny resource-allocation problem.“Sometimes, in a field like optimization, it is very common for folks to think of solutions as either purely machine learning or purely classical. I am a firm believer that we want to get the best of both worlds, and this is a really strong instantiation of that hybrid approach,” says senior author Cathy Wu, the Gilbert W. Winslow Career Development Assistant Professor in Civil and Environmental Engineering (CEE), and a member of a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society (IDSS).Wu wrote the paper with co-lead authors Sirui Li, an IDSS graduate student, and Wenbin Ouyang, a CEE graduate student; as well as Max Paulus, a graduate student at ETH Zurich. The research will be presented at the Conference on Neural Information Processing Systems.Tough to SolveMILP problems have an exponential number of potential solutions. For instance, say a traveling salesperson wants to find the shortest path to visit several cities and then return to their city of origin. If there are many cities that could be visited in any order, the number of potential solutions might be greater than the number of atoms in the universe.“These problems are called NP-hard, which means it is very unlikely there is an efficient algorithm to solve them. When the problem is big enough, we can only hope to achieve some suboptimal performance,” Wu explains.An MILP solver employs an array of techniques and practical tricks that can achieve reasonable solutions in a tractable amount of time.A typical solver uses a divide-and-conquer approach, first splitting the space of potential solutions into smaller pieces with a technique called branching. Then, the solver employs a technique called cutting to tighten up these smaller pieces so they can be searched faster.Cutting uses a set of rules that tighten the search space without removing any feasible solutions. These rules are generated by a few dozen algorithms, known as separators, that have been created for different kinds of MILP problems.Wu and her team found that the process of identifying the ideal combination of separator algorithms to use is, in itself, a problem with an exponential number of solutions.“Separator management is a core part of every solver, but this is an underappreciated aspect of the problem space. One of the contributions of this work is identifying the problem of separator management as a machine learning task to begin with,” she says.Shrinking the Solution SpaceShe and her collaborators devised a filtering mechanism that reduces this separator search space from more than 130,000 potential combinations to around 20 options. This filtering mechanism draws on the principle of diminishing marginal returns, which says that the most benefit would come from a small set of algorithms, and adding additional algorithms won’t bring much extra improvement.Then they use a machine-learning model to pick the best combination of algorithms from among the 20 remaining options.This model is trained with a dataset specific to the user’s optimization problem, so it learns to choose algorithms that best suit the user’s particular task. Since a company like FedEx has solved routing problems many times before, using real data gleaned from past experience should lead to better solutions than starting from scratch each time.The model’s iterative learning process, known as contextual bandits, a form of reinforcement learning, involves picking a potential solution, getting feedback on how good it was, and then trying again to find a better solution.This data-driven approach accelerated MILP solvers between 30 and 70 percent without any drop in accuracy. Moreover, the speedup was similar when they applied it to a simpler, open-source solver and a more powerful, commercial solver.In the future, Wu and her collaborators want to apply this approach to even more complex MILP problems, where gathering labeled data to train the model could be especially challenging. Perhaps they can train the model on a smaller dataset and then tweak it to tackle a much larger optimization problem, she says. The researchers are also interested in interpreting the learned model to better understand the effectiveness of different separator algorithms.Reference: “Learning to Configure Separators in Branch-and-Cut” by Sirui Li, Wenbin Ouyang, Max B. Paulus, Cathy Wu, 8 November 2023, Mathematics > Optimization and Control.arXiv:2311.05650This research is supported, in part, by Mathworks, the National Science Foundation (NSF), the MIT Amazon Science Hub, and MIT’s Research Support Committee.

Citizen science leads the charge in environmental protection

In a compelling movement, ordinary citizens are stepping up to tackle environmental challenges through citizen science, significantly contributing to research and data collection efforts worldwide. Andrew Kersley reports for Wired.In short:Citizen scientists in Ilkley, UK, driven by the neglect of official bodies, have successfully identified harmful levels of pollution in their local river, leading to its recognition as a protected bathing water site.This grassroots effort exemplifies a global trend where individuals, motivated by a lack of official support and the availability of affordable technology, are becoming pivotal in monitoring environmental health.Safecast, a nonprofit, has harnessed the power of volunteer efforts to create a vast open database of environmental data, demonstrating the potential for citizen science to influence global environmental policy.Key quote: “Citizen science doesn’t just let people collect data, it empowers them and gives them a voice.”— Steffen Fritz, International Institute for Applied Systems AnalyticsWhy this matters: The rise of citizen science not only fills gaps left by underfunded and politically constrained scientific research but also fosters a more democratic and participatory approach to science. Leah Segedie, who runs the blog and wellness community Mamavation, was dubbed the "PFAS Hunter" by Consumer Reports for her work testing products for evidence of PFAS chemicals.

In a compelling movement, ordinary citizens are stepping up to tackle environmental challenges through citizen science, significantly contributing to research and data collection efforts worldwide. Andrew Kersley reports for Wired.In short:Citizen scientists in Ilkley, UK, driven by the neglect of official bodies, have successfully identified harmful levels of pollution in their local river, leading to its recognition as a protected bathing water site.This grassroots effort exemplifies a global trend where individuals, motivated by a lack of official support and the availability of affordable technology, are becoming pivotal in monitoring environmental health.Safecast, a nonprofit, has harnessed the power of volunteer efforts to create a vast open database of environmental data, demonstrating the potential for citizen science to influence global environmental policy.Key quote: “Citizen science doesn’t just let people collect data, it empowers them and gives them a voice.”— Steffen Fritz, International Institute for Applied Systems AnalyticsWhy this matters: The rise of citizen science not only fills gaps left by underfunded and politically constrained scientific research but also fosters a more democratic and participatory approach to science. Leah Segedie, who runs the blog and wellness community Mamavation, was dubbed the "PFAS Hunter" by Consumer Reports for her work testing products for evidence of PFAS chemicals.

Creative collisions: Crossing the art-science divide

A collaboration between ACT and MIT.nano, the class 4.373/4.374 (Creating Art, Thinking Science) asks what it really takes to cultivate dialogue between disciplines.

MIT has a rich history of productive collaboration between the arts and the sciences, anchored by the conviction that these two conventionally opposed ways of thinking can form a deeply generative symbiosis that serves to advance and humanize new technologies.  This ethos was made tangible when the Bauhaus artist and educator György Kepes established the MIT Center for Advanced Visual Studies (CAVS) within the Department of Architecture in 1967. CAVS has since evolved into the Art, Culture, and Technology (ACT) program, which fosters close links to multiple other programs, centers, and labs at MIT. Class 4.373/4.374 (Creating Art, Thinking Science), open to undergraduates and master’s students of all disciplines as well as certain students from the Harvard Graduate School of Design (GSD), is one of the program’s most innovative offerings, proposing a model for how the relationship between art and science might play out at a time of exponential technological growth.  Now in its third year, the class is supported by an Interdisciplinary Class Development Grant from the MIT Center for Art, Science and Technology (CAST) and draws upon the unparalleled resources of MIT.nano; an artist’s high-tech toolbox for investigating the hidden structures and beauty of our material universe. High ambitions and critical thinking The class was initiated by Tobias Putrih, lecturer in ACT, and is taught with the assistance of Ardalan SadeghiKivi MArch ’23, and Aubrie James SM ’24. Central to the success of the class has been the collaboration with co-instructor Vladimir Bulović, the founding director of MIT.nano and Fariborz Maseeh Chair in Emerging Technology, who has positioned the facility as an open-access resource for the campus at large — including MIT’s community of artists. “Creating Art, Thinking Science” unfolds the 100,000 square feet of cleanroom and lab space within the Lisa T. Su Building, inviting participating students to take advantage of cutting-edge equipment for nanoscale visualization and fabrication; in the hands of artists, devices for discovering nanostructures and manipulating atoms become tools for rendering the invisible visible and deconstructing the dynamics of perception itself.  The expansive goals of the class are tempered by an in-built criticality. “ACT has a unique position as an art program nested within a huge scientific institute — and the challenges of that partnership should not be underestimated,” reflects Putrih. “Science and art are wholly different knowledge systems with distinct historical perspectives. So, how do we communicate? How do we locate that middle ground, that third space?” An evolving answer, tested and developed throughout the partnership between ACT and MIT.nano, involves a combination of attentive mentorship and sharing of artistic ideas, combined with access to advanced technological resources and hands-on practical training.  “MIT.nano currently accommodates more than 1,200 individuals to do their work, across 250 different research groups,” says Bulović. “The fact that we count artists among those is a matter of pride for us. We’ve found that the work of our scientists and technologists is enhanced by having access to the language of art as a form of expression — equally, the way that artists express themselves can be stretched beyond what could previously be imagined, simply by having access to the tools and instruments at MIT.nano.” A playground for experimentation True to the spirit of the scientific method and artistic iteration, the class is envisioned as a work in progress — a series of propositions and prototypes for how dialogue between scientists and artists might work in practice. The outcomes of those experiments can now be seen installed in the first and second floor galleries at MIT.nano. As part of the facility’s five-year anniversary celebration, the class premiered an exhibition showcasing works created during previous years of “Creating Art, Thinking Science.”  Visitors to the exhibition, “zero.zerozerozerozerozerozerozerozeroone” (named for the numerical notation for one nanometer), will encounter artworks ranging from a minimalist silicon wafer produced with two-photon polymerization (2PP) technology (“Obscured Invisibility,” 2021, Hyun Woo Park), to traces of an attempt to make vegetable soup in the cleanroom using equipment such as a cryostat, a fluorescing microscope, and a Micro-CT scanner (“May I Please Make You Some Soup?,” 2022, Simone Lasser).  These works set a precedent for the artworks produced during the fall 2023 iteration of the class. For Ryan Yang, in his senior year studying electrical engineering and computer science at MIT, the chance to engage in open discussion and experimental making has been a rare opportunity to “try something that might not work.” His project explores the possibilities of translating traditional block printing techniques to micron-scale 3D-printing in the MIT.nano labs. Yang has taken advantage of the arts curriculum at MIT at an early stage in his academic career as an engineer; meanwhile, Ameen Kaleem started out as a filmmaker in New Delhi and is now pursuing a master’s degree in design engineering at Harvard GSD, cross-registered at MIT.  Kaleem’s project models the process of abiogenesis (the evolution of living organisms from inorganic or inanimate substances) by bringing living moss into the MIT.nano cleanroom facilities to be examined at an atomic scale. “I was interested in the idea that, as a human being in the cleanroom, you are both the most sanitized version of yourself and the dirtiest thing in that space,” she reflects. “Drawing attention to the presence of organic life in the cleanroom is comparable to bringing art into spaces where it might not otherwise exist — a way of humanizing scientific and technological endeavors.” Consciousness, immersion, and innovation The students draw upon the legacies of landmark art-science initiatives — including international exhibitions such as “Cybernetic Serendipity” (London ICA, 1968), the “New Tendencies” series (Zagreb, 1961-73), and “Laboratorium” (Antwerp, 1999) — and take inspiration from the instructors’ own creative investigations of the inner workings of different knowledge systems. “In contemporary life, and at MIT in particular, we’re immersed in technology,” says Putrih. “It’s the nature of art to reveal that to us, so that we might see the implications of what we are producing and its potential impact.” By fostering a mindset of imagination and criticality, combined with building the technical skills to address practical problems, “Creating Art, Thinking Science” seeks to create the conditions for a more expansive version of technological optimism; a culture of innovation in which social and environmental responsibility are seen as productive parameters for enriched creativity. The ripple effects of the class might be years in the making, but as Bulović observes while navigating the exhibition at MIT.nano, “The joy of the collaboration can be felt in the artworks.”

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